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院校 题目 类型 日期 作者 摘要 网页
HKUST Simulation-based evolutionary optimization for energy-efficient layout plan design of high-rise residential buildings Journal -- Gan, V.J.L., Wong, H.K., Tse, K.T., Cheng, J.C.P., Lo, I.M.C., and Chan, C.M. Buildings consume 40% of global energy, in which residential buildings account for a significant proportion of the total energy used. Previous studies have attempted to optimize the layout plan of residential buildings for minimizing the total energy usage, mainly focusing on low-rise houses of a regular shape and having a limited number of design variables. However, layout design for high-rise residential buildings involves the complicated interaction among a large number of design variables (e.g., different types of flats with varying configurations) under practical design constraints. The number of possible solutions may increase exponentially which calls for new optimization strategies. Therefore, this study aims to develop an energy performance-based optimization approach to identify the most energy-efficient layout plan design for high-rise residential buildings. A simulation-based optimization method applying the evolutionary genetic algorithm (GA) is developed to systematically explore the best layout design for maximizing the building energy efficiency. In an illustrative example, the proposed optimization approach is applied to generate the layout plan for a 40-storey public housing in Hong Kong. The results indicate that GA attempts to maximize the use of natural-occurring energy sources (e.g., wind-driven natural ventilation and sunlight) for minimizing 30–40% of the total energy consumption associated with air-conditioning and lighting. The optimization approach provides a decision support basis for achieving substantial energy conservation in high-rise residential buildings, thereby contributing to a sustainable built environment. 连结
HKUST Integrating 4D BIM and GIS for construction supply chain management Journal 02/2019 Deng, Y., Gan, V.J.L., Das, M., Cheng, J.C.P., and Anumba, C.J. Construction supply chain management (CSCM) requires the tracking of material logistics and construction activities, an integrated platform, and certain coordination mechanisms among CSCM participants. Researchers have suggested the use of building information modeling (BIM) technology to monitor construction activities and manage construction supply chains. However, because material warehousing and deliveries are mostly performed outside construction project sites, project information from a single BIM model is insufficient in meeting the needs of construction supply chain management. In this research, an integrated framework was developed based on four-dimensional (4D) BIM and a geographical information system (GIS) for coordination of construction supply chains between the construction project sites and other project related locations, such as supplier sites and material consolidation centers. The proposed integration was used to solve three common tasks in CSCM, namely (1) supplier selection, (2) determination of number of material deliveries, and (3) allocation of consolidation centers, using information from 4D BIM and GIS. The proposed 4D BIM-GIS framework was demonstrated via case studies. The results of the case studies indicated that determinations of supplier and number of deliveries need to take into account both the transportation distance and material unit price. Mathematical solutions were also generated to support decision making for the allocation of consolidation centers in congested regions with long transportation distances. The outcomes of this paper serve as a decision support base for a more efficient CSCM in the future. 连结
HKUST Automated Clash-free Steel Reinforcement Design in RC Structures Using BIM and GA Report 06/2018 Tommy Yuen Currently, building information modelling (BIM) technology has been increasingly popular in the architecture, engineering and construction (AEC) industry for some years, but it is not widely adopted in structural design. The objective of this project is to develop a framework for automated rebar design in RC beams using the building information modelling (BIM) technology.

This project presents an automated rebar design program, based on the latest BIM technology. Design constraints for the optimization are considered according to the Hong Kong Code of Practice. The developed program will make use of the analysis result from structural software to design RC beams, and then the tailor-made genetic algorithm will optimize the final rebar design. Finally, generate the rebars to BIM model in 3D environment.

The result shows that BIM can carry out repetitive works and complicated calculations automatically and accurately. Unlike human, they seldom make mistakes through over-tiredness, do not require rest breaks and can carry out in seconds what may take hours to do by manual methods. The overall design process is fully automated, smooth and without error. Therefore, it is anticipated that the time and manpower resource required for structural design and management could be reduced significantly.
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HKUST Automated optimization of steel reinforcement in RC building frames using building information modeling and hybrid genetic algorithm Journal 02/2018 Mangal, M., and Cheng, J.C.P. Design of steel reinforcement is an important and necessary task for designing reinforced concrete (RC) building structures. Currently, steel reinforcement design is performed manually or semi-automatically using computer software such as ETABS, with reference to building codes. These approaches are time consuming and sometimes error-prone. Recent advances in building information modeling (BIM) technology allow digital 3D BIM models to be leveraged for supporting different types of engineering analyses such as structural engineering design. With the aid of BIM technology, steel reinforcement design could be automated for fast, economical and error-free procedures. This paper presents a BIM-based framework using the developed three-stage hybrid genetic algorithm (GA) for automated optimization of steel reinforcement in RC frames. The methodology framework determines the selection and alignment of steel reinforcement bars in an RC building frame for the minimum steel reinforcement area, considering longitudinal tensile, longitudinal compressive and shear steel reinforcement. The first two stages optimize the longitudinal tensile and longitudinal compressive steel reinforcement while the third stage optimizes the shear steel reinforcement. International design code (BS8110) and buildability constraints are considered in the developed optimization framework. A BIM model in Industry Foundation Classes (IFC) is then automatically created to visualize the optimized steel reinforcement design results in 3D thereby facilitating design communication and generation of construction detailing drawings. A three-storey RC building frame is analyzed to check the applicability of the developed framework and its improvement over current design approaches. The results show that the developed methodology framework can minimize the steel reinforcement area quickly and accurately. 连结
HKUST Parametric modeling and evolutionary optimization for cost-optimal and low-carbon design of high-rise reinforced concrete buildings Journal 07/2019 Gan, V.J.L., Wong, C.L., Tse, K.T., Cheng, J.C.P., Lo, I.M.C., and Chan, C.M. Design optimization of reinforced concrete structures helps reducing the global carbon emissions and the construction cost in buildings. Previous studies mainly targeted at the optimization of individual structural elements in low-rise buildings. High-rise reinforced concrete buildings have complicated structural designs and consume tremendous amounts of resources, but the corresponding optimization techniques were not fully explored in literature. Furthermore, the relationship between the optimization of individual structural elements and the topological arrangement of the entire structure is highly interactive, which calls for new optimization methods. Therefore, this study aims to develop a novel optimization approach for cost-optimal and low-carbon design of high-rise reinforced concrete structures, considering both the structural topology and individual element optimizations. Parametric modelling is applied to define the relationship between individual structural members and the behavior of the entire building structure. A novel evolutionary optimization technique using the genetic algorithm is proposed to optimize concrete building structures, by first establishing the optimal structural topology and then optimizing individual member sizes. In an illustrative example, a high-rise reinforced concrete building is used to examine the proposed optimization approach, which can systematically explore alternative structural designs and identify the optimal solution. It is shown that the carbon emissions and material cost are both reduced by 18–24% after performing optimization. The proposed approach can be extended to optimize other types of buildings (such as steel framework) with a similar problem nature, thereby improving the cost efficiency and environmental sustainability of the built environment. 连结
HKUST Social BIMCloud – A Distributed Cloud-based BIM Framework for Object-based Lifecycle Information Exchange and Supply Chain Integration Thesis 08/2015 Moumita DAS Due to its fragmented and multi-domain architecture, the AEC (architecture, engineering, and construction) industry faces the issues of data transfer efficiency and data consistency while exchanging large BIM files. In this thesis, a cloud based BIM framework, called Social BIMCloud is presented for building design and management of lifecycle activities. Social BIMCloud addresses the issue of data transfer efficiency by reducing the size of the BIM files being exchanged through dynamic splitting and merging mechanisms. Data consistency is also improved by hosting a common integrated BIM model which is updated partially instead of generating a new BIM file for every new change, which usually leads to data duplicity. This collaborative framework, Social BIMCloud is termed “Social” in particular, as it captures and manages the formal and informal social interactions that take place in a construction project. The methodology for capturing and managing social interactions through Social BIMCloud has been demonstrated in this thesis by integrating it with popular BIM software, Autodesk Revit.

Social BIMCloud provides the scope for extending and integrating it with external planning and analysis applications in a plug-and-play manner for lifecycle integration. In this thesis, methodologies and demonstrations have been presented for extending and integrating Social BIMCloud for – (1) construction supply chain (CSC), (2) green building design, and (3) construction site layout planning. For CSC integration, an ontology based web service framework is presented. Ontologies incorporate data semantics in the information exchanged. Therefore, the information exchanging parties, i.e. software applications in the case of automatic information exchange, comprehend the meaning of the information and therefore facilitate smooth flow of heterogeneous information. Two example ontologies have developed by studying the CSC and those ontologies have been used to enrich the data model of Social BIMCloud for accommodating and supporting CSC integration.

Popular energy simulation software were studied to design and extend the schema of Social BIMCloud in order to integrate it with standard simulation and analysis engines through a web service based framework. Social BIMCloud has also been extended for managing construction logistics by integrating it with a construction site layout planning (CSLP) engine. For this integration, the data model of Social BIMCloud has been extended for construction schedule information like activity start date, end date and the relation of each activity with one or more building elements and the vice versa. Finally this thesis discusses the scope of future extensions and improvements on Social BIMCloud for facilitating smooth flow of information in the construction industry.
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